Publications (17)
Resource Distribution Under Spatiotemporal Uncertainty of Disease Spread: Stochastic versus Robust Approaches
Beste Basciftci, Xian Yu, Siqian Shen
We consider the problem of optimizing locations of distribution centers (DCs) and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertaintie…
Contextual Stochastic Optimization with Decision-Dependent Uncertainty via Nonparametric Learning
Huangrong Sun, Xian Yu
The paper proposes a framework for solving decision-dependent contextual stochastic optimization problems by learning uncertainty with nonparametric regression models and incorpora…
On the Value of Risk-Averse Multistage Stochastic Programming in Capacity Planning
Xian Yu, Siqian Shen
We consider a risk-averse stochastic capacity planning problem under uncertain demand in each period. Using a scenario tree representation of the uncertainty, we formulate a multis…
A Gauge Set Framework for Flexible Robustness Design
Ningji Wei, Xian Yu, Peter Zhang
This paper proposes a unified framework for designing robustness in optimization under uncertainty using gauge sets, convex sets that generalize distance and capture how distributi…
On the Global Convergence of Risk-Averse Natural Policy Gradient Methods with Expected Conditional Risk Measures
Xian Yu, Lei Ying
Risk-sensitive reinforcement learning (RL) has become a popular tool for controlling the risk of uncertain outcomes and ensuring reliable performance in highly stochastic sequentia…
Distributionally Robust Optimization for Chemotherapy Scheduling under Asymmetric and Multi-Modal Uncertainty
Qing Zhu, Xian Yu, Yu-Li Huang
We consider a real-world chemotherapy scheduling template design problem, where we cluster patient types into groups and find a representative time-slot duration for each group to…
Reward Redistribution via Gaussian Process Likelihood Estimation
Minheng Xiao, Xian Yu
In many practical reinforcement learning tasks, feedback is only provided at the end of a long horizon, leading to sparse and delayed rewards. Existing reward redistribution method…
On the Value of Multistage Risk-Averse Stochastic Facility Location With or Without Prioritization
Xian Yu, Siqian Shen
We consider a multiperiod stochastic capacitated facility location problem under uncertain demand and budget in each period. Using a scenario tree representation of the uncertainti…
Residuals-based Offline Reinforcement Learning
Qing Zhu, Xian Yu
Offline reinforcement learning (RL) has received increasing attention for learning policies from previously collected data without interaction with the real environment, which is p…
Multistage Distributionally Robust Mixed-Integer Programming with Decision-Dependent Moment-Based Ambiguity Sets
Xian Yu, Siqian Shen
We study multistage distributionally robust mixed-integer programs under endogenous uncertainty, where the probability distribution of stage-wise uncertainty depends on the decisio…
Learning to Cut: Reinforcement Learning for Benders Decomposition
Haochen Cai, Xian Yu
Benders decomposition (BD) is a widely used solution approach for solving two-stage stochastic programs arising in real-world decision-making under uncertainty. However, it often s…
Kernel-based Regularized Iterative Learning Control of Repetitive Linear Time-varying Systems
Xian Yu, Xiaozhu Fang, Biqiang Mu +1
For data-driven iterative learning control (ILC) methods, both the model estimation and controller design problems are converted to parameter estimation problems for some chosen mo…
Risk-Averse Reinforcement Learning via Dynamic Time-Consistent Risk Measures
Xian Yu, Siqian Shen
Traditional reinforcement learning (RL) aims to maximize the expected total reward, while the risk of uncertain outcomes needs to be controlled to ensure reliable performance in a…
Distributionally Robust Optimization with Multimodal Decision-Dependent Ambiguity Sets
Xian Yu, Beste Basciftci
We consider a two-stage distributionally robust optimization (DRO) model with multimodal uncertainty, where both the mode probabilities and uncertainty distributions could be affec…
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence
Minheng Xiao, Xian Yu, Lei Ying
Risk-sensitive reinforcement learning (RL) is crucial for maintaining reliable performance in high-stakes applications. While traditional RL methods aim to learn a point estimate o…
An Optimization-and-Simulation Framework for Redesigning University Campus Bus System with Social Distancing
Gongyu Chen, Xinyu Fei, Huiwen Jia +2
The outbreak of coronavirus disease 2019 (COVID-19) has led to significant challenges for schools, workplaces and communities to return to operations during the pandemic, requiring…
Residuals-Based Contextual Distributionally Robust Optimization with Decision-Dependent Uncertainty: Theoretical Guarantees and Decomposition Algorithm
Qing Zhu, Xian Yu, Guzin Bayraksan
We consider a residuals-based distributionally robust optimization (DRO) model, where the underlying uncertainty depends on both covariate information and our decisions. We adopt b…